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筛选

浏览/检索结果: 共543条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Parcel-Level Mapping of Artificial Forests Along the Middle Reach Valley of Yarlung Tsangpo River Based on Deep Learning Algorithms 期刊论文  OAI收割 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2025, 卷号: 22, 页码: 5 作者:  Xia, Changshuo;  Zhao, Wei;  Tan, Jianbo;  Wu, Tianjun;  Ding, Tao   |  收藏  |  浏览/下载：17/0  |  提交时间：2025/04/08 Forests  Accuracy  Feature extraction  Image segmentation  Training  Rivers  Deep learning  Remote sensing  Level set  Data mining  Artificial forests (AFs)  deep learning (DL)  image segmentation  Yarlung Tsangpo River   CG-CFPANet: a multi-task network for built-up area extraction from SDGSAT-1 and Sentinel-2 remote sensing images 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2024, 卷号: 17, 期号: 1, 页码: 19 作者:  Wang, Lei;  Ye, Cheng;  Chen, Fang;  Wang, Ning;  Li, Congrong   |  收藏  |  浏览/下载：69/0  |  提交时间：2024/03/04 Built-up area  deep learning  remote sensing image  SDGSAT-1  semantic segmentation   Multinetwork Algorithm for Coastal Line Segmentation in Remote Sensing Images 期刊论文  OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 卷号: 62, 页码: 4208312 作者:     |  收藏  |  浏览/下载：117/0  |  提交时间：2024/09/19 Sea measurements  Image segmentation  Feature extraction  Remote sensing  Transformers  Training  Generators  Coastal line  generative adversarial network (GAN)  remote sensing  segmentation  transformer  Unet   Vectorization Method for Remote Sensing Object Segmentation Based on Frame Field Learning: A Case Study of Greenhouses 期刊论文  OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 卷号: 62, 页码: 5625414 作者:  Yao, Ling;  Lu, Yuxiang;  Liu, Tang;  Jiang, Hou;  Zhou, Chenghu   |  收藏  |  浏览/下载：14/0  |  提交时间：2024/07/12 Greenhouses  Vectors  Remote sensing  Image segmentation  Image edge detection  Feature extraction  Activate contour model  deep learning  frame field learning  greenhouse   基于自监督神经网络的遥感图像配准方法研究 学位论文  OAI收割 2024 作者:  周雨欣   |  收藏  |  浏览/下载：24/0  |  提交时间：2024/07/01 Remote sensing image  image registration  deep learning  feature extraction   A Novel Dynamic Contextual Feature Fusion Model for Small Object Detection in Satellite Remote-Sensing Images 期刊论文  OAI收割 Information (Switzerland), 2024, 卷号: 15, 期号: 4 作者:  Yang, Hongbo;  Qiu, Shi   |  收藏  |  浏览/下载：10/0  |  提交时间：2024/08/15 small object detection  satellite remote-sensing image processing  computer vision  deep learning   Mask2Former with Improved Query for Semantic Segmentation in Remote-Sensing Images 期刊论文  OAI收割 MATHEMATICS, 2024, 卷号: 12, 期号: 5, 页码: 24 作者:  Guo, Shichen;  Yang, Qi;  Xiang, Shiming;  Wang, Shuwen;  Wang, Xuezhi   |  收藏  |  浏览/下载：26/0  |  提交时间：2024/07/03 semantic segmentation  remote-sensing image  transformer  Mask2Former  query   Duplex-Hierarchy Representation Learning for Remote Sensing Image Classification 期刊论文  OAI收割 Sensors, 2024, 卷号: 24, 期号: 4 作者:  Yuan, Xiaobin;  Zhu, Jingping;  Lei, Hao;  Peng, Shengjun;  Wang, Weidong   |  收藏  |  浏览/下载：17/0  |  提交时间：2024/04/08 remote sensing image classification  duplex hierarchy  discriminative representation  confusion score   Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model 期刊论文  OAI收割 REMOTE SENSING, 2024, 卷号: 16, 期号: 2, 页码: 16 作者:  Wang, Baoguo;  Yao, Yonghui   |  收藏  |  浏览/下载：19/0  |  提交时间：2024/03/01 vegetation classification  multi-source image  remote sensing  semantic segmentation  multi-channel model  deep learning   CLDRNet: A Difference Refinement Network Based on Category Context Learning for Remote Sensing Image Change Detection 期刊论文  OAI收割 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 卷号: 17, 页码: 2133-2148 作者:  Wan, Ling;  Tian, Ye;  Kang, Wenchao;  Ma, Lei   |  收藏  |  浏览/下载：17/0  |  提交时间：2024/02/20 Feature extraction  Task analysis  Remote sensing  Transformers  Deep learning  Semantics  Support vector machines  Category context learning (CCL)  clustering learning (CL)  difference map refinement (DMR)  optical remote sensing image  change detection (CD)